# How to Avoid rarely used discrete feature values in a dataset

On Google's ML crash course it states:

Good feature values should appear more than 5 or so times in a data set. Doing so enables a model to learn how this feature value relates to the label. That is, having many examples with the same discrete value gives the model a chance to see the feature in different settings, and in turn, determine when it's a good predictor for the label.

This make sense, but if that happens what should we do? For example, consider a dataset with street_name as feature and out 2000 rows, only 4 of them have street_name equal to "Learning St.".

Should we remove those rows containing rare feature values? Or what?

Any insights would be greatly appreciated.